A Community Benchmark for the Automated Segmentation of Pediatric Neuroblastoma on Multi-Modal MRI: Design and Results of the SPPIN Challenge at MICCAI 2023 Article Swipe
Myrthe A. D. Buser
,
Dominique C. Simons
,
Matthijs Fitski
,
Marc H. W. A. Wijnen
,
Annemieke S. Littooij
,
Annemiek H. ter Brugge
,
Iris N. Vos
,
Markus H. A. Janse
,
Mathijs de Boer
,
Rens ter Maat
,
Junya Sato
,
Shoji Kido
,
Satoshi Kondo
,
Satoshi Kasai
,
Marek Wodziński
,
Henning Müller
,
Jin Ye
,
Junjun He
,
Yannick Kirchhoff
,
Maximilian Rokuss
,
Gao Haokai
,
Matías Fernández-Patón
,
Diana Veiga-Canuto
,
David Ellis
,
Michele R. Aizenberg
,
Bas H. M. van der Velden
,
Hugo J. Kuijf
,
Alberto De Luca
,
Alida F. W. van der Steeg
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12111157
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12111157
Surgery plays a key role in treating neuroblastoma. To assist surgical planning, anatomical 3D models derived from the segmentation of anatomical structures on MRI scans are often used. Automation using deep learning can make segmentations less time-consuming and more reliable. We organized the Surgical Planning in PedIatric Neuroblastoma (SPPIN) challenge, to stimulate developments and benchmarking of automatic segmentation of neuroblastoma on MRI. SPPIN is the first segmentation challenge in extracranial pediatric oncology. Nine teams provided a valid submission. Evaluation was based on the Dice similarity coefficient (Dice score), the 95th percentile of the Hausdorff distance (HD95), and the volumetric similarity (VS). A combination of these scores determined the ranking of the teams. The spread in the median evaluation scores per team was large (Dice: 0.21–0.82; HD95: 63.31–7.69; VS: 0.31–0.91). The top-performing team achieved a median Dice score of 0.82 (with an HD95 of 7.69 mm and a VS of 0.91) using a large, pre-trained model. However, in the pre-operative segmentations, significantly lower evaluation scores were observed. Our results indicate that pre-training might be useful in small, pediatric datasets. Although the general results of the winning team were high, they were insufficient to use for surgical planning in small, pre-operative tumors.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering12111157
- https://www.mdpi.com/2306-5354/12/11/1157/pdf?version=1761461024
- OA Status
- gold
- References
- 26
- OpenAlex ID
- https://openalex.org/W4415560199
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4415560199Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bioengineering12111157Digital Object Identifier
- Title
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A Community Benchmark for the Automated Segmentation of Pediatric Neuroblastoma on Multi-Modal MRI: Design and Results of the SPPIN Challenge at MICCAI 2023Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-26Full publication date if available
- Authors
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Myrthe A. D. Buser, Dominique C. Simons, Matthijs Fitski, Marc H. W. A. Wijnen, Annemieke S. Littooij, Annemiek H. ter Brugge, Iris N. Vos, Markus H. A. Janse, Mathijs de Boer, Rens ter Maat, Junya Sato, Shoji Kido, Satoshi Kondo, Satoshi Kasai, Marek Wodziński, Henning Müller, Jin Ye, Junjun He, Yannick Kirchhoff, Maximilian Rokuss, Gao Haokai, Matías Fernández-Patón, Diana Veiga-Canuto, David Ellis, Michele R. Aizenberg, Bas H. M. van der Velden, Hugo J. Kuijf, Alberto De Luca, Alida F. W. van der SteegList of authors in order
- Landing page
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https://doi.org/10.3390/bioengineering12111157Publisher landing page
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https://www.mdpi.com/2306-5354/12/11/1157/pdf?version=1761461024Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2306-5354/12/11/1157/pdf?version=1761461024Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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26Number of works referenced by this work
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